#001 Import and Plot data for one stock:¶

Define and import data, save as CSV¶

Import libraries

  • Link to Pandas Documentation: https://pandas.pydata.org/docs/index.html

  • Link to datareader Documentation: https://pandas-datareader.readthedocs.io/en/latest/index.html

  • Link to Yf Documentation: https://pandas-datareader.readthedocs.io/en/latest/readers/yahoo.html

  • Link to Plotly Documentation: https://plotly.com/python/

In [1]:
#    !pip install pandas
#    !pip install pandas-datareader
#    !pip install yfinance
#    !pip install datetime
#    !pip install plotly_express
In [2]:
import pandas as pd
from pandas_datareader import data as pdr
import numpy as np
import yfinance as yf
import datetime as dt
import plotly.express as px
import plotly.graph_objects as go
In [3]:
# Define the start and end dates, last 5 years
end = dt.datetime.now()
start = end - dt.timedelta(days = 365*5)
In [4]:
# define Tickers
tk = input('Enter the ticker code: ')
tickers = [tk]
file_name = tk + ".csv"

yf.pdr_override()

Enter the ticker code: MSFT
In [5]:
#obtain data and save as CSV
df = pdr.get_data_yahoo(tickers, start = start, end = end)
df.to_csv(file_name)
df.head(2)

[*********************100%%**********************]  1 of 1 completed
Out[5]:
Open High Low Close Adj Close Volume
Date
2018-11-12 109.419998 109.959999 106.099998 106.870003 101.251991 33621800
2018-11-13 107.550003 108.739998 106.639999 106.940002 101.318344 35374600

Import CSV and test values¶

In [6]:
#read CSV file
stock_df = pd.read_csv(file_name)
In [7]:
# Test for null values, and show info
stock_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1258 entries, 0 to 1257
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       1258 non-null   object 
 1   Open       1258 non-null   float64
 2   High       1258 non-null   float64
 3   Low        1258 non-null   float64
 4   Close      1258 non-null   float64
 5   Adj Close  1258 non-null   float64
 6   Volume     1258 non-null   int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 68.9+ KB

Calculate Daily Returns from one stock, and insert on DataFrame¶

In [8]:
#insert 'Daily Return' column
stock_df['Daily Return'] = stock_df['Adj Close'].pct_change(1) * 100

# replace the first row, changes null for 0
stock_df['Daily Return'].replace(np.nan, 0, inplace = True)
stock_df
Out[8]:
Date Open High Low Close Adj Close Volume Daily Return
0 2018-11-12 109.419998 109.959999 106.099998 106.870003 101.251991 33621800 0.000000
1 2018-11-13 107.550003 108.739998 106.639999 106.940002 101.318344 35374600 0.065532
2 2018-11-14 108.099998 108.260002 104.470001 104.970001 99.881516 39495100 -1.418133
3 2018-11-15 104.989998 107.800003 103.910004 107.279999 102.079544 38505200 2.200636
4 2018-11-16 107.080002 108.879997 106.800003 108.290001 103.040596 33502100 0.941474
... ... ... ... ... ... ... ... ...
1253 2023-11-06 353.450012 357.540009 353.350006 356.529999 356.529999 23828300 1.057259
1254 2023-11-07 359.399994 362.459991 357.630005 360.529999 360.529999 25833900 1.121925
1255 2023-11-08 361.679993 363.869995 360.549988 363.200012 363.200012 26767800 0.740580
1256 2023-11-09 362.299988 364.790009 360.359985 360.690002 360.690002 24847300 -0.691082
1257 2023-11-10 361.489990 370.100006 361.070007 369.670013 369.670013 28042100 2.489676

1258 rows × 8 columns

In [9]:
stock_df.describe().round(2)
Out[9]:
Open High Low Close Adj Close Volume Daily Return
count 1258.00 1258.00 1258.00 1258.00 1258.00 1.258000e+03 1258.00
mean 229.14 231.57 226.66 229.22 225.05 2.988689e+07 0.12
std 71.73 72.40 71.04 71.73 72.76 1.269096e+07 1.95
min 95.14 97.97 93.96 94.13 89.57 8.989200e+06 -14.74
25% 162.03 163.63 160.41 162.10 156.59 2.185582e+07 -0.85
50% 238.38 242.36 235.74 239.61 236.64 2.669715e+07 0.12
75% 287.82 289.95 284.04 287.69 283.81 3.387148e+07 1.12
max 362.30 370.10 361.07 369.67 369.67 1.112421e+08 14.22

Ploting Results¶

Used Plotly Express for data visualization

In [10]:
# Define a function that performs interactive data visualization using Plotly Express
def plotly_data(df, title):  
    
    # Create figure
    fig = go.Figure()
   
    # Set title
    fig.update_layout(title_text = title) 
    
    # For loop that plots all stock prices in the pandas dataframe df
    # starts with 1, to skip the date column
    
    for i in df.columns[1:]:
        # Add range slider
        fig.update_layout(xaxis=dict(rangeselector = dict(buttons=list([dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all")])), rangeslider=dict( visible=True), type="date"))
        # Add line graph
        fig.add_scatter(x = df['Date'], y = df[i], name = i)
        # Update Layout
        fig.update_layout({'plot_bgcolor': "white"})
    fig.show()
In [11]:
# Ploting AdjClose
plotly_data(stock_df.iloc[:, [0 , 5]], (tk + ' - Ajusted Closing Price[$]'))

# Ploting Trade Volume
plotly_data(stock_df.iloc[:, [0 , 6]], (tk +' - Trading Volume'))

# Ploting Percentage Daily Return
plotly_data(stock_df.iloc[:, [0 , 7]], (tk +' - Percentage Daily Return [%]'))
In [ ]: